Effects of Nested Interruptions on Task Resumption: A Laboratory Study With Intensive Care Nurses
Bibliographic record
Abstract
OBJECTIVE: Interruptions to secondary tasks resulting in multiple tasks to resume may tax working memory. The objective of this research is to study such interruptions experienced by intensive care unit (ICU) nurses. BACKGROUND: ICU nurses are frequently interrupted, resulting in a switch from primary to secondary tasks. In two recent studies, we observed that some of these secondary tasks also get interrupted, resulting in multiple tasks that have to be resumed, a phenomenon we refer to as nested interruptions. Although completing multiple secondary tasks in a serial fashion during an interruption period can create context-switching costs, we hypothesize that nested interruptions tax the working memory even more than just performing multiple secondary tasks sequentially because the nurse would have to encode in working memory the resumption goals for both the primary and the interrupted secondary tasks. METHOD: We conducted a laboratory study with 30 ICU nurses, who performed an electronic order-entry task under three interruption conditions: (a) baseline-no secondary task during the interruption period; (2) serial-performance of two tasks one after the other during the interruption period; and (3) nested-performance of two tasks during the interruption period, one of which was also interrupted. RESULTS: Nested interruptions resulted in significantly longer primary-task resumption lag and less accurate task resumption compared with both the serial interruption and baseline conditions. CONCLUSION: The nested nature of interruptions adds to the resumption lag and diminishes resumption accuracy by likely populating the working memory with goals associated with interrupted secondary tasks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".